A method for small-sized wheat seedlings detection: from annotation mode to model construction.

The number of seedlings is an important indicator that reflects the size of the wheat population during the seedling stage. Researchers increasingly use deep learning to detect and count wheat seedlings from unmanned aerial vehicle (UAV) images. However, due to the small size and diverse postures of wheat seedlings, it can be challenging to estimate their numbers accurately during the seedling stage. In most related works in wheat seedling detection, they label the whole plant, often resulting in a higher proportion of soil background within the annotated bounding boxes. This imbalance between wheat seedlings and soil background in the annotated bounding boxes decreases the detection performance. This study proposes a wheat seedling detection method based on a local annotation instead of a global annotation. Moreover, the detection model is also improved by replacing convolutional and pooling layers with the Space-to-depth Conv module and adding a micro-scale detection layer in the YOLOv5 head network to better extract small-scale features in these small annotation boxes. The optimization of the detection model can reduce the number of error detections caused by leaf occlusion between wheat seedlings and the small size of wheat seedlings. The results show that the proposed method achieves a detection accuracy of 90.1%, outperforming other state-of-the-art detection methods. The proposed method provides a reference for future wheat seedling detection and yield prediction.

Small and Oriented Wheat Spike Detection at the Filling and Maturity Stages Based on WheatNet.

Accurate wheat spike detection is crucial in wheat field phenotyping for precision farming. Advances in artificial intelligence have enabled deep learning models to improve the accuracy of detecting wheat spikes. However, wheat growth is a dynamic process characterized by important changes in the color feature of wheat spikes and the background. Existing models for wheat spike detection are typically designed for a specific growth stage. Their adaptability to other growth stages or field scenes is limited. Such models cannot detect wheat spikes accurately caused by the difference in color, size, and morphological features between growth stages. This paper proposes WheatNet to detect small and oriented wheat spikes from the filling to the maturity stage. WheatNet constructs a Transform Network to reduce the effect of differences in the color features of spikes at the filling and maturity stages on detection accuracy. Moreover, a Detection Network is designed to improve wheat spike detection capability. A Circle Smooth Label is proposed to classify wheat spike angles in drone imagery. A new micro-scale detection layer is added to the network to extract the features of small spikes. Localization loss is improved by Complete Intersection over Union to reduce the impact of the background. The results show that WheatNet can achieve greater accuracy than classical detection methods. The detection accuracy with average precision of spike detection at the filling stage is 90.1%, while it is 88.6% at the maturity stage. It suggests that WheatNet is a promising tool for detection of wheat spikes.

NULISA: a proteomic liquid biopsy platform with attomolar sensitivity and high multiplexing.

The blood proteome holds great promise for precision medicine but poses substantial challenges due to the low abundance of most plasma proteins and the vast dynamic range of the plasma proteome. Here we address these challenges with NUcleic acid Linked Immuno-Sandwich Assay (NULISA™), which improves the sensitivity of traditional proximity ligation assays by ~10,000-fold to attomolar level, by suppressing assay background via a dual capture and release mechanism built into oligonucleotide-conjugated antibodies. Highly multiplexed quantification of both low- and high-abundance proteins spanning a wide dynamic range is achieved by attenuating signals from abundant targets with unconjugated antibodies and next-generation sequencing of barcoded reporter DNA. A 200-plex NULISA containing 124 cytokines and chemokines and other proteins demonstrates superior sensitivity to a proximity extension assay in detecting biologically important low-abundance biomarkers in patients with autoimmune diseases and COVID-19. Fully automated NULISA makes broad and in-depth proteomic analysis easily accessible for research and diagnostic applications.

Effects of pre-anthesis low-temperature stress on the mineral components in wheat grains.

Introduction: The nutritional value of wheat is important to human health. Despite minerals being essential nutrients for the human body, they are often neglected in consideration of the nutritional quality of cereal grains. Extreme low-temperature events have become more frequent due to the current environmental unpredictability, and it is yet unknown how the mineral components in grains are affected by low temperature. Methods: To provide valuable information for enhancing the nutritional quality of wheat under potential climatic conditions, we treated different cold-sensitive wheat cultivars at four low-temperature levels during the individual and combined stages of jointing and booting in controlled-environment phytotrons. Results and Discussion: In general, the contents of P, K, Ca, and Zn in the cold-sensitive cultivar (Yangmai16) and K in the cold-tolerant cultivar (Xumai30) were enhanced by low temperature. However, the accumulation of minerals in mature grains was reduced under low-temperature treatment, except for P, Ca, and Zn in Yangmai16. In addition, the mineral content and accumulation in Yangmai16 (except for Fe) were more susceptible to low temperature during the combined stages, while the mineral content and accumulation of K, Fe, and Zn in Xumai30 were more susceptible to low temperature during the booting stage. Moreover, Yangmai16 under extremely low temperatures (T3 and T4) during booting and Xumai30 under all low-temperature treatments during the combined stages had lower comprehensive evaluation values. These findings offer a crucial reference for enhancing the nutritional quality of wheat grains under climate change.

Improving multi-scale detection layers in the deep learning network for wheat spike detection based on interpretive analysis.

BACKGROUND: Detecting and counting wheat spikes is essential for predicting and measuring wheat yield. However, current wheat spike detection researches often directly apply the new network structure. There are few studies that can combine the prior knowledge of wheat spike size characteristics to design a suitable wheat spike detection model. It remains unclear whether the complex detection layers of the network play their intended role. RESULTS: This study proposes an interpretive analysis method for quantitatively evaluating the role of three-scale detection layers in a deep learning-based wheat spike detection model. The attention scores in each detection layer of the YOLOv5 network are calculated using the Gradient-weighted Class Activation Mapping (Grad-CAM) algorithm, which compares the prior labeled wheat spike bounding boxes with the attention areas of the network. By refining the multi-scale detection layers using the attention scores, a better wheat spike detection network is obtained. The experiments on the Global Wheat Head Detection (GWHD) dataset show that the large-scale detection layer performs poorly, while the medium-scale detection layer performs best among the three-scale detection layers. Consequently, the large-scale detection layer is removed, a micro-scale detection layer is added, and the feature extraction ability in the medium-scale detection layer is enhanced. The refined model increases the detection accuracy and reduces the network complexity by decreasing the network parameters. CONCLUSION: The proposed interpretive analysis method to evaluate the contribution of different detection layers in the wheat spike detection network and provide a correct network improvement scheme. The findings of this study will offer a useful reference for future applications of deep network refinement in this field.

NULISA: a novel proteomic liquid biopsy platform with attomolar sensitivity and high multiplexing.

The blood proteome holds great promise for precision medicine but poses substantial challenges due to the low abundance of most plasma proteins and the vast dynamic range across the proteome. We report a novel proteomic technology - NUcleic acid Linked Immuno-Sandwich Assay (NULISA™) - that incorporates a dual capture and release mechanism to suppress the assay background and improves the sensitivity of the proximity ligation assay by over 10,000-fold to the attomolar level. It utilizes pairs of antibodies conjugated to DNA oligonucleotides that enable immunocomplex purification and generate reporter DNA containing target- and sample-specific barcodes for a next-generation sequencing-based, highly multiplexed readout. A 200-plex NULISA targeting 124 cytokines and chemokines and 80 other immune response-related proteins demonstrated superior sensitivity for detecting low-abundance proteins and high concordance with other immunoassays. The ultrahigh sensitivity allowed the detection of previously difficult-to-detect, but biologically important, low-abundance biomarkers in patients with autoimmune diseases and COVID-19. Fully automated NULISA addresses longstanding challenges in proteomic analysis of liquid biopsies and makes broad and in-depth proteomic analysis accessible to the general research community and future diagnostic applications.

The development of multifunctional sulfated polyguluronic acid-based polymeric micelles for anticancer drug delivery.

Numerous disseminated tumor cells specifically overexpress P-selectin. Therefore, it was thought to be a potential target for tumor therapy. Herein, we described a novel P-selectin-targeted glycosyl ligand-sulfated polyguluronic acid (PGS), as an oriented carrier of P-selectin-targeted drug delivery system. Specifically, the PGS-SS-DOX polymeric micelles were constructed to confirm the practicability of the PGS carrier as a new P-selectin-targeted ligand. PGS-SS-DOX micelles comprised P-selectin-targeted PGS, doxorubicin (DOX) as an anticarcinogen, and pH/redox dual-sensitive bio-linker facilitating drug release in tumor tissues. In vitro and in vivo data showed that PGS-SS-DOX micelles significantly increased tumor cell killing capacity and exhibited a favorable biocompatibility comparison with Free-DOX. This work proved that PGS was an ideal low immunogenic, biodegradable drug carrier for the delivery of anti-cancer drugs. The facile PGS-SS-drug micelle system provided enormous opportunities for treating disseminated tumors utilizing many irreplaceable anticarcinogens.

Piezo protein determines stem cell fate by transmitting mechanical signals.

Piezo ion channel is a mechanosensitive protein on the cell membrane, which contains Piezo1 and Piezo2. Piezo channels are activated by mechanical forces, including stretch, matrix stiffness, static pressure, and shear stress. Piezo channels transmit mechanical signals that cause different downstream responses in the differentiation process, including integrin signaling pathway, ERK1/2 MAPK signaling pathway, Notch signaling, and WNT signaling pathway. In the fate of stem cell differentiation, scientists found differences in Piezo channel expression and found that Piezo channel expression is related to developmental diseases. Here, we briefly review the structure and function of Piezo channels and the relationship between Piezo and mechanical signals, discussing the current understanding of the role of Piezo channels in stem cell fate and associated molecules and developmental diseases. Ultimately, we believe this review will help identify the association between Piezo channels and stem cell fate.

Alterations in Spontaneous Brain Activity in Drug-Naïve First-Episode Schizophrenia: An Anatomical/Activation Likelihood Estimation Meta-Analysis.

OBJECTIVE: The etiology of schizophrenia is unknown and is associated with abnormal spontaneous brain activity. There are no consistent results regarding the change in spontaneous brain activity of people with schizophrenia. In this study, we determined the specific changes in the amplitude of low-frequency fluctuation/fractional amplitude of low-frequency fluctuation (ALFF/fALFF) and regional homogeneity (ReHo) in patients with drug-naïve first-episode schizophrenia (Dn-FES). METHODS: A comprehensive search of databases such as PubMed, Web of Science, and Embase was conducted to find articles on resting-state functional magnetic resonance imaging using ALFF/fALFF and ReHo in schizophrenia patients compared to healthy controls (HCs) and then, anatomical/activation likelihood estimation was performed. RESULTS: Eighteen eligible studies were included in this meta-analysis. Compared to the spontaneous brain activity of HCs, we found changes in spontaneous brain activity in Dn-FES based on these two methods, mainly including the frontal lobe, putamen, lateral globus pallidus, insula, cerebellum, and posterior cingulate cortex. CONCLUSION: We found that widespread abnormalities of spontaneous brain activity occur in the early stages of the onset of schizophrenia and may provide a reference for the early intervention of schizophrenia.

Development of a Highly Sensitive Neurofilament Light Chain Assay on an Automated Immunoassay Platform.

Background: Neurofilament light chain (NfL) is an axonal cytoskeletal protein that is released into the extracellular space following neuronal or axonal injury associated with neurological conditions such as multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS), and other diseases. NfL is detectable in the cerebrospinal fluid (CSF) and blood. Numerous studies on MS have demonstrated that NfL is correlated with disease activity, predicts disease progression, and is reduced by treatment with MS disease-modifying drugs, making NfL an attractive candidate to supplement existing clinical and imaging measures in MS. However, for NfL to achieve its potential as a clinically useful biomarker for clinical decision-making or drug development, a standardized, practical, and widely accessible assay is needed. Our objective was to develop a novel NfL assay on an automated, globally available immunoassay platform and validate its performance. Methods: A prototype NfL assay was first developed and evaluated on the ADVIA Centaur® XP immunoassay system from Siemens Healthineers. The lower limit of quantitation (LLoQ), within-lab precision, assay range, cross-reactivity with neurofilament medium and heavy chains, and effect of interfering substances were determined. NfL assay values in serum and CSF were compared with radiological and clinical disease activity measures in patients with MS and ALS, respectively. This assay was further optimized to utilize serum, plasma, and CSF sample types on the Atellica® IM system and transferred to Siemens' CLIA laboratory where it was analytically validated as a laboratory-developed test (LDT). Results: In this study, an LLoQ of 1.85 pg/mL, within-lab precision <6%, and an assay range of up to 646 pg/mL were demonstrated with the serum prototype assay. Cross-reactivity of <0.7% with the neurofilament medium and heavy chains was observed. Serum and CSF NfL assay values were associated with radiological and clinical disease activity measures in patients with MS and ALS, respectively. The optimized version of the NfL assay demonstrated specimen equivalence with additional plasma tube types and was analytically validated as an LDT. Conclusion: The analytical performance of the NfL assay fulfilled all acceptance criteria; therefore, we suggest that the assay is acceptable for use in both research and clinical practice settings to determine elevated NfL levels in patients.

The Relationship Between Abnormal Resting-State Functional Connectivity of the Left Superior Frontal Gyrus and Cognitive Impairments in Youth-Onset Drug-Naïve Schizophrenia.

Objective: Age of onset is one of the heterogeneous factors in schizophrenia, and an earlier onset of the disease indicated a worse prognosis. The left superior frontal gyrus (SFG) is involved in numerous cognitive and motor control tasks. Hence, we explored the relationship between abnormal changes in SFG resting-state functional connectivity (rsFC) and cognitive function in the peak age of incidence to understand better the pathophysiological mechanism in youth-onset drug-naïve schizophrenia to search for reliable biomarkers. Methods: About 66 youth-onset drug-naïve schizophrenia patients and 59 healthy controls (HCs) were included in this study. Abnormal connectivity changes in the left SFG and whole brain were measured using the region of interest (ROI) rsFC analysis method. The cognitive function was assessed using the MATRICS Consensus Cognitive Battery (MCCB), and the severity of the clinical symptoms was evaluated by positive and negative syndrome scale (PANSS). Furthermore, we analyzed the relationships among abnormal FC values, cognition scores, and clinical symptoms. Results: We found decreased FC between left SFG and bilateral precuneus (PCUN), right hippocampus, right parahippocampal gyrus, left thalamus, left caudate, insula, and right superior parietal lobule (SPL), whereas increased FC was seen between the left SFG and right middle frontal gyrus (MFG) in the youth-onset drug-naïve schizophrenia group, compared with HCs. Meanwhile, the T-scores were lower in each cognitive domain than HCs. Moreover, in the youth-onset drug-naive schizophrenia group, the insula was negatively correlated with processing speed. No significant correlations were found between the FC-value and PANSS score. Conclusions: Our findings suggest widespread FC network abnormalities in the left SFG and widespread cognitive impairments in the early stages of schizophrenia. The dysfunctional connectivity of the left SFG may be a potential pathophysiological mechanism in youth-onset drug-naïve schizophrenia.

Regional Homogeneity Brain Alterations in Schizophrenia: An Activation Likelihood Estimation Meta-Analysis.

OBJECTIVE: Resting state functional magnetic resonance imaging (rsfMRI) provides a lot of evidence for local abnormal brain activity in schizophrenia, but the results are not consistent. Our aim is to find out the consistent abnormal brain regions of the patients with schizophrenia by using regional homogeneity (ReHo), and indirectly understand the degree of brain damage of the patients with drug-naive first episode schizophrenia (Dn-FES) and chronic schizophrenia. METHODS: We performed the experiment by activation likelihood estimation (ALE) software to analysis the differences between people with schizophrenia group (all schizophrenia group and chronic schizophrenia group) and healthy controls. RESULTS: Thirteen functional imaging studies were included in quantitative meta-analysis. All schizophrenia group showed decreased ReHo in bilateral precentral gyrus (PreCG) and left middle occipital gyrus (MOG), and increased ReHo in bilateral superior frontal gyrus (SFG) and right insula. Chronic schizophrenia group showed decreased ReHo in bilateral MOG, right fusiform gyrus, left PreCG, left cerebellum, right precuneus, left medial frontal gyrus and left anterior cingulate cortex (ACC). No significant increased brain areas were found in patients with chronic schizophrenia. CONCLUSION: Our findings suggest that patients with chronic schizophrenia have more extensive brain damage than FES, which may contribute to our understanding of the progressive pathophysiology of schizophrenia.

Preparation and Evaluation of a Self-Nanoemulsifying Drug Delivery System Loaded with Heparin Phospholipid Complex.

A self-nanoemulsifying drug delivery system (SNEDDS) was developed to enhance the absorption of heparin after oral administration, in which heparin was compounded with phospholipids to achieve better fat solubility in the form of heparin-phospholipid (HEP-Pc) complex. HEP-Pc complex was prepared using the solvent evaporation method, which increased the solubility of heparin in n-octanol. The successful preparation of HEP-Pc complex was confirmed by differential scanning calorimetry (DSC), Fourier-transform infrared (FT-IR) spectroscopy, NMR, and SEM. A heparin lipid microemulsion (HEP-LM) was prepared by high-pressure homogenization and characterized. HEP-LM can enhance the absorption of heparin after oral administration, significantly prolong activated partial thromboplastin time (APTT) and thrombin time (TT) in mice, and reduce fibrinogen (FIB) content. All these outcomes indicate that HEP-LM has great potential as an oral heparin formulation.

An automatic method for counting wheat tiller number in the field with terrestrial LiDAR.

BACKGROUND: The tiller number per unit area is one of the main agronomic components in determining yield. A real-time assessment of this trait could contribute to monitoring the growth of wheat populations or as a primary phenotyping indicator for the screening of cultivars for crop breeding. However, determining tiller number has been conventionally dependent on tedious and labor-intensive manual counting. In this study, an automatic tiller-counting algorithm was developed to estimate the tiller density under field conditions based on terrestrial laser scanning (TLS) data. The novel algorithm, which is named ALHC, involves two steps: (1) the use of an adaptive layering (AL) algorithm for cluster segmentation and (2) the use of a hierarchical clustering (HC) algorithm for tiller detection among the clusters. Three field trials during the 2016-2018 wheat seasons were conducted to validate the algorithm with twenty different wheat cultivars, three nitrogen levels, and two planting densities at two ecological sites (Rugao & Xuzhou) in Jiangsu Province, China. RESULT: The results demonstrated that the algorithm was promising across different cultivars, years, growth stages, planting densities, and ecological sites. The tests from Rugao and Xuzhou in 2016-2017 and Rugao in 2017-2018 showed that the algorithm estimated the tiller number of the wheat with regression coefficient (R2) values of 0.61, 0.56 and 0.65, respectively. In short, tiller counting with the ALHC generally underestimated the tiller number and performed better for the data with lower plant densities, compact plant types and the jointing stage, which were associated with overlap and noise between plants and inside the dense canopy. CONCLUSIONS: Differing from the previous methods, the ALHC proposed in this paper made full use of 3D crop information and developed an automatic tiller counting method that is suitable for the field environment.

VGLL4 Protects against Oxidized-LDL-Induced Endothelial Cell Dysfunction and Inflammation by Activating Hippo-YAP/TEAD1 Signaling Pathway.

Vestigial-like 4 (VGLL4) has been found to have multiple functions in tumor development; however, its role in cardiovascular disease is unknown. The aim of this study was to investigate the effect of VGLL4 on the dysfunction and inflammatory response of Ox-LDL-induced human umbilical vein endothelial cells (HUVECs) and its mechanism, so as to provide a new theoretical basis for the diagnosis and treatment of atherosclerosis. In the present study, the protective activity of VGLL4 inhibiting Ox-LDL-induced apoptosis, oxidative stress, inflammation, and injury as well as its molecular mechanisms was examined using human umbilical vein endothelial cells (HUVECs). The results showed that the expression of VGLL4 was decreased with the increase of Ox-LDL concentration in HUVECs. In addition, the functional study found that VGLL4 overexpression alleviated Ox-LDL-induced oxidative stress, inflammation, and dysfunction and inhibited apoptosis. Further research found that VGLL4 regulated Hippo-YAP/TEAD1 signaling pathway, and the Hippo-YAP/TEAD1 signaling pathway was involved in the protective mechanism of VGLL4 on HUVECs. In conclusion, it suggests that VGLL4 protects against oxidized-LDL-induced endothelial cell dysfunction by activating the Hippo-YAP/TEAD1 signaling pathway.

Development of a Critical Nitrogen Dilution Curve of Double Cropping Rice in South China.

The concept of critical nitrogen (Nc) concentration can be implemented to diagnose in-season plant nitrogen (N) status for optimizing N fertilizer management. The Nc dilution curves have been established for rice (Oryza sativa L.) grown in different climatic regions, yet no attempt has been made to develop the Nc dilution curve for double cropping rice regions. This study was undertaken to develop the Nc dilution curves for double cropping rice in south China for assessment of in-season N status and to establish the relationships N nutrition index (NNI) and relative yield (RY) for in-season prediction of rice grain yield. Three different N application rate field experiments using six Indica rice varieties, including two early rice hybrids and four late rice hybrids were carried out in east China. The Nc dilution curves based on whole plant N concentration were determined and described as, Nc = 3.37 W-0.44 for early rice and Nc = 3.69 W-0.34 for late rice. The constant N concentration at early growth stage was 3.31 and 3.15% DM for early and late rice, respectively. Late rice showed a higher capacity of N accumulation and a lower rate of N decline per unit shoot biomass as compared to early rice. The curves for present study were different from the existing reference curves for Indica and Japonica rice grown in different rice growing regions. Integrated N nutrition index (NNIint) based on Nc was used to estimate RY at different growth periods using linear regression functions. The results showed that the critical curves and relationship between NNIint and RY could be used as a reliable indicator of N status diagnosis, grain yield prediction as well as to provide technical support in N management for double cropping rice in south China.

[Walking Ability Assessment System for Parkinson's Patients Based on Inertial Sensor].

In order to evaluate the ability of Parkinson's patients to walk comprehensively, a system based on MEMS to aid clinical quantification of ability in Parkinson's is established. The inertial units are respectively fixed on the back and the waist of subject to be measured. The Kalman fusion algorithm is used to extract the characteristic parameters of accelerometer and gyroscope data. SVM classifier is designed to train and test the classifier by the feature. The results show that the system possesses a high recognition rate for Parkinson's patients and normal subjects and for the classification of the walking ability of patients with Parkinson's disease. So, this system can aid doctors to give more object diagnostic conclusion.

[Digital System for the Geriatric Somatic Function Assessment].

At present, the somatic function assessment of the elderly mostly depends on the doctor's observation and the scale evaluation, and there isn't the objective and quantitative assessment criteria. In this paper, we design and construct a digital system for the geriatric somatic function assessment, which is based on digital ground, inertial unit, mobile terminal and PC terminal. The gait and balance parameters of the elderly were obtained by the Time Up and Go test and five postures test. Based on the linear regression model of gait and balance parameters, the system was used to evaluate the somatic function of the elderly. The experimental results show that the system not only can accurately distinguish elderly between fall and non-fall, but also has a high correlation between the score of the system and the score of clinical geriatric physical function scale.

[Effects of different cultivation patterns on soil aggregates and organic carbon fractions].

Combined with the research in an organic farm in the past 10 years, differences of soil aggregates composition, distribution and organic carbon fractions between organic and conventional cultivation were studied by simultaneous sampling analysis. The results showed that the percentages of aggregates (> 1 mm, 1-0.5 mm, 0.5-0.25 mm and < 0.25 mm) in the conventional cultivation were 23.75%, 15.15%, 19.98% and 38.09%, while those in organic cultivation were 9.73%, 18.41%, 24.46% and 43.90%, respectively. The percentage of < 0.25 mm micro-aggregates was significantly higher in organic cultivation than that in conventional cultivation. Organic cultivation increased soil organic carbon (average of 17.95 g x kg(-1)) and total nitrogen contents (average of 1.51 g x kg(-1)). Among the same aggregates in organic cultivation, the average content of heavy organic carbon fraction was significantly higher than that in conventional cultivation. This fraction accumulated in < 0. 25 mm micro-aggregates, which were main storage sites of stable organic carbon. In organic cultivation, the content of labile organic carbon in > 1 mm macro-aggregates was significantly higher than that in conventional cultivation, while no significant difference was found among the other aggregates, indicating that the labile organic carbon was enriched in > 1 mm macro-aggregates. Organic cultivation increased the amounts of organic carbon and its fractions, reduced tillage damage to aggregates, and enhanced the stability of organic carbon. Organic cultivation was therefore beneficial for soil carbon sequestration. The findings of this research may provide theoretical basis for further acceleration of the organic agriculture development.